Background
This data set has been designed by Josh Mannix as a part of JOUR 301.
The data shows the correlation between players having record setting numbers as far as yards and the amount of opportunities they receive.
Some of the more intresting take aways…
While rushing numbers and rushing attempts had an obvious relationship for running backs it did not translate to receivers or quarterbacks. Many of the receivers put up big numbers with far fewer attempts than 13. On the other end there were many QB’s with far more attempts than while very few ever eclipsed the 500 yard mark.
The other trend that I noticed was the rise in passing number over the past 2 decades. In the early 2000’s it was realtivley rare to see a passer over 57 attempts, but recentely it has been far more common.
Data
Code for obtaining data…library(tidyverse)
library(tidytuesdayR)
NFL_Data <-tidytuesdayR::tt_load("2018-08-28")
nfl_stats <- NFL_Data$`nfl_2010-2017`glimpse(nfl_stats)Rows: 81,525
Columns: 23
$ ...1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
$ name <chr> "Duce Staley", "Lamar Smith", "Tiki Barber", "Stephen Dav…
$ team <chr> "PHI", "MIA", "NYG", "WAS", "IND", "BAL", "NYJ", "MIN", "…
$ game_year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 200…
$ game_week <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ rush_att <dbl> 26, 27, 13, 23, 28, 27, 30, 14, 15, 10, 20, 13, 23, 14, 2…
$ rush_yds <dbl> 201, 145, 144, 133, 124, 119, 110, 109, 88, 87, 84, 80, 7…
$ rush_avg <dbl> 7.7, 5.4, 11.1, 5.8, 4.4, 4.4, 3.7, 7.8, 5.9, 8.7, 4.2, 6…
$ rush_tds <dbl> 1, 1, 2, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 3, …
$ rush_fumbles <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 1, 1, …
$ rec <dbl> 4, 1, 3, 4, 6, 4, 6, 2, 2, NA, 4, 3, 1, 4, 1, 1, 1, NA, N…
$ rec_yds <dbl> 61, 12, 25, 37, 40, 32, 34, 3, 20, NA, 29, 10, -2, 100, 1…
$ rec_avg <dbl> 15.3, 12.0, 8.3, 9.3, 6.7, 8.0, 5.7, 1.5, 10.0, NA, 7.3, …
$ rec_tds <dbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, NA, 0, 0, 0, 1, 0, 0, 0, NA, N…
$ rec_fumbles <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, N…
$ pass_att <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 41, NA, NA, NA, NA, N…
$ pass_yds <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 290, NA, NA, NA, NA, …
$ pass_tds <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ int <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA…
$ sck <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ pass_fumbles <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA…
$ rate <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 102.7, NA, NA, NA, NA…
$ position <chr> "RB", "RB", "RB", "RB", "RB", "RB", "RB", "RB", "RB", "QB…hi_rsh <- filter(nfl_stats, rush_yds > 200) %>%
select(name, rush_yds, starts_with("game"))
hi_rsh %>%
count(game_year)# A tibble: 17 × 2
game_year n
<dbl> <int>
1 2000 10
2 2001 2
3 2002 4
4 2003 3
5 2004 2
6 2005 3
7 2006 4
8 2007 3
9 2008 2
10 2009 5
# … with 7 more rowsglimpse(nfl_stats)Rows: 81,525
Columns: 23
$ ...1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
$ name <chr> "Duce Staley", "Lamar Smith", "Tiki Barber", "Stephen Dav…
$ team <chr> "PHI", "MIA", "NYG", "WAS", "IND", "BAL", "NYJ", "MIN", "…
$ game_year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 200…
$ game_week <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ rush_att <dbl> 26, 27, 13, 23, 28, 27, 30, 14, 15, 10, 20, 13, 23, 14, 2…
$ rush_yds <dbl> 201, 145, 144, 133, 124, 119, 110, 109, 88, 87, 84, 80, 7…
$ rush_avg <dbl> 7.7, 5.4, 11.1, 5.8, 4.4, 4.4, 3.7, 7.8, 5.9, 8.7, 4.2, 6…
$ rush_tds <dbl> 1, 1, 2, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 3, …
$ rush_fumbles <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 1, 1, …
$ rec <dbl> 4, 1, 3, 4, 6, 4, 6, 2, 2, NA, 4, 3, 1, 4, 1, 1, 1, NA, N…
$ rec_yds <dbl> 61, 12, 25, 37, 40, 32, 34, 3, 20, NA, 29, 10, -2, 100, 1…
$ rec_avg <dbl> 15.3, 12.0, 8.3, 9.3, 6.7, 8.0, 5.7, 1.5, 10.0, NA, 7.3, …
$ rec_tds <dbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, NA, 0, 0, 0, 1, 0, 0, 0, NA, N…
$ rec_fumbles <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, N…
$ pass_att <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 41, NA, NA, NA, NA, N…
$ pass_yds <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 290, NA, NA, NA, NA, …
$ pass_tds <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ int <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA…
$ sck <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ pass_fumbles <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA…
$ rate <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 102.7, NA, NA, NA, NA…
$ position <chr> "RB", "RB", "RB", "RB", "RB", "RB", "RB", "RB", "RB", "QB…hi_pass <-filter(nfl_stats, pass_yds > 500) %>%
select(name, pass_yds, starts_with("game"))
hi_pass %>%
count(game_year)# A tibble: 10 × 2
game_year n
<dbl> <int>
1 2000 1
2 2006 1
3 2009 1
4 2011 1
5 2012 2
6 2013 1
7 2014 1
8 2015 2
9 2016 2
10 2017 1glimpse(nfl_stats)Rows: 81,525
Columns: 23
$ ...1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
$ name <chr> "Duce Staley", "Lamar Smith", "Tiki Barber", "Stephen Dav…
$ team <chr> "PHI", "MIA", "NYG", "WAS", "IND", "BAL", "NYJ", "MIN", "…
$ game_year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 200…
$ game_week <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ rush_att <dbl> 26, 27, 13, 23, 28, 27, 30, 14, 15, 10, 20, 13, 23, 14, 2…
$ rush_yds <dbl> 201, 145, 144, 133, 124, 119, 110, 109, 88, 87, 84, 80, 7…
$ rush_avg <dbl> 7.7, 5.4, 11.1, 5.8, 4.4, 4.4, 3.7, 7.8, 5.9, 8.7, 4.2, 6…
$ rush_tds <dbl> 1, 1, 2, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 3, …
$ rush_fumbles <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 1, 1, …
$ rec <dbl> 4, 1, 3, 4, 6, 4, 6, 2, 2, NA, 4, 3, 1, 4, 1, 1, 1, NA, N…
$ rec_yds <dbl> 61, 12, 25, 37, 40, 32, 34, 3, 20, NA, 29, 10, -2, 100, 1…
$ rec_avg <dbl> 15.3, 12.0, 8.3, 9.3, 6.7, 8.0, 5.7, 1.5, 10.0, NA, 7.3, …
$ rec_tds <dbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, NA, 0, 0, 0, 1, 0, 0, 0, NA, N…
$ rec_fumbles <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, N…
$ pass_att <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 41, NA, NA, NA, NA, N…
$ pass_yds <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 290, NA, NA, NA, NA, …
$ pass_tds <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ int <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA…
$ sck <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ pass_fumbles <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA…
$ rate <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 102.7, NA, NA, NA, NA…
$ position <chr> "RB", "RB", "RB", "RB", "RB", "RB", "RB", "RB", "RB", "QB…hi_rec <-filter(nfl_stats, rec_yds > 200) %>%
select(name, pass_yds, starts_with("game"))
hi_pass %>%
count(game_year)# A tibble: 10 × 2
game_year n
<dbl> <int>
1 2000 1
2 2006 1
3 2009 1
4 2011 1
5 2012 2
6 2013 1
7 2014 1
8 2015 2
9 2016 2
10 2017 1Information about the dataset…
Graphs
Code for creating graphs…
labs_grp_bubble <- labs(
title = "Rushing Attempts Leaders",
x = "Year", y = "Name",
size = "Rushing Yards")
ggp2_grp_bubble <- filter(nfl_stats,
rush_att > 33) |>
ggplot(aes(
x = game_year,
y = name)) +
geom_point(
aes(size = rush_yds,
fill= name),
show.legend = FALSE,
alpha = 2/3,
shape = 21,
color = "black") +
scale_size(range = c(1,7),
name = "rushing yards")+
ggthemes::theme_few(
base_size = 11)
#color is a fill=rush_yds inside the AES funtcion
ggp2_grp_bubble +
labs_grp_bubbleThe size of the bubble is relative to the amount of attempts above 33.
Graphs
Code for creating graph…
labs_scatter <- labs(
title = "Highest Rushers",
x = "game_year", y = "name)")
ggp2_scatter <- filter(
nfl_stats, rush_yds > 200)|>
ggplot(
aes(x = game_year,
y = name)) +
geom_point()
ggp2_scatter +
labs_scatterGraphs
Code for creating graph…
labs_grp_bubble <- labs(
title = "Receptions >13",
x = "Year", y = "Name",
size = "Rushing Yards")
ggp2_grp_bubble <- filter(
nfl_stats, rec > 13) |>
ggplot(aes(
x = game_year, y = name)) +
geom_point(
aes(size = rush_yds, fill= name),
show.legend = FALSE,
alpha = 2/3, shape = 21,
color = "black") +
scale_size(range = c(1,7),
name = "rushing yards")+
ggthemes::theme_few()
#color is a fill=rush_yds inside the AES funtcion
ggp2_grp_bubble +
labs_grp_bubbleThe Size of the bubble is relative to the amount of receptions above 13.
Graphs
Code for creating graph…
labs_scatter <- labs(
title = "Highest Recivers",
x = "game_year", y = "name)")
ggp2_scatter <- filter(
nfl_stats, rec_yds > 200)|>
ggplot(
aes(x = game_year,
y = name)) +
geom_point()
ggp2_scatter +
labs_scatterGraphs
Code for creating graph…
labs_grp_bubble <- labs(
title = "Pass Attempt Leaders",
x = "Year", y = "Name",
size = "Rushing Yards")
ggp2_grp_bubble <- filter(
nfl_stats, pass_att > 57) |>
ggplot(aes(
x = game_year, y = name)) +
geom_point(
aes(size = rush_yds, fill= name),
show.legend = FALSE,
alpha = 2/3, shape = 21,
color = "black") +
scale_size(range = c(1,7),
name = "rushing yards")+
ggthemes::theme_few()
#color is a fill=rush_yds inside the AES funtcion
ggp2_grp_bubble +
labs_grp_bubbleThe size of the bubble is relative to the amount of pass attmepts over 57.
Graphs
Code for creating graph…
labs_scatter <- labs(
title = "Highest Passers",
x = "game_year", y = "name)")
ggp2_scatter <- filter(
nfl_stats, pass_yds > 500)|>
ggplot(
aes(x = game_year,
y = name)) +
geom_point()
ggp2_scatter +
labs_scatterGraphs for data…